After spending on a while on this, I finally managed to detect the hands through thresholding. The only problem is that VERY FEW pixels in the background remain, which will mess up the next step. Any suggestions on how to get rid of the few background pixels? Because I don't want to go through the whole background subtraction thing for just a few pixels. Background Subtraction is not an option for the program, so please don't suggest it
Thanks
It's hard to be sure without a more detailed description of your hand detection algorithm. If you have a few background pixels that are isolated from the hands you have detected, I would suggest morphological operation like opening to eliminate single pixel detections in your binary mask. In openCV, I think you need to erode and then dilate
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So I am fairly inexperienced with After Effects. But I am making a title for a video project and am having a hard time figuring something out. I have some text that slides over, and appears once it is in the masked area. However this is a very hard edge and doesn't look the best. How do I soften that edge? I tried making another shape and blurring it and then masking that over the other shape but that didn't work. I tried Googling but it was full of people saying you cannot feather a shapes edges in AE.
I would use the mask in my image directly with the feather option but then that mask moves with my image, and that's not what I want.
So I learned that you can just make an image in photoshop that does whatever you want it to do and just use that.
The masks feathering moves your image? Can you put up a screen recording of the problem?
I am working on a project in which my task is to find malfunctioning detector-pixels. I thought that this problem is really similar to the problems people facing, when trying to detect bad pixels on an image. Right now I have maps, that have good and bad detector pixels. The way to find out if a detector part is bad is the following: if collects different data then the other non-malfunctioning pixels around it, then it probably is malfunctioning. However, in my case, the bad pixels tend to be next to each other clumping up, and I don't really know how I should interpret this. Can someone help me out with a good algorithm, or a book that is helpful?
This is how a map looks:
These should be found:
If you have multiple images from the same sensor, and there are bad pixels at the same place, you can detect them by comparing images pixel by pixel. This will allow you to detect places that does not change (probably bad pixel).
Other idea may be using something like Gauss-Filter and then compare this blurred image with original one.
Good Idea will be loading some images to Gimp or Photoshop and try some filters and then if you will find good way to spot the bad pixels - implement it by yourself. I would recommend OpenCV for this task.
OpenCV has lots of build-in mechanism. Some of them (maybe edge-detection? blurring?) may be interesting for you.
I want to ask about what kind of problems there be if i use this method to extract foreground.
The condition before using this method is that it runs on fixed camera so there're not going to be any movement on camera position.
And what im trying to do is below.
read one frame from camera and set this frame as background image. This is done periodically.
periodically subtract frames that are read afterward to background image above. Then there will be only moving things colored differently from other area
that are same to background image.
then isolate moving object by using grayscale, binarization, thresholding.
iterate above 4 processes.
If i do this, would probability of successfully detect moving object be high? If not... could you tell me why?
If you consider illumination change(gradually or suddenly) in scene, you will see that your method does not work.
There are more robust solutions for these problems. One of these(maybe the best) is Gaussian Mixture Model applied for background subtraction.
You can use BackgroundSubtractorMOG2 (implementation of GMM) in OpenCV library.
Your scheme is quite adequate to cases where the camera is fix and the background is stationary. Indoor and man-controlled scenes are more appropriate to this approach than outdoor and natural scenes .I've contributed to a detection system that worked basically on the same principals you suggested. But of course the details are crucial. A few remarks based on my experience
Your initialization step can cause very slow convergence to a normal state. You set the background to the first frames, and then pieces of background coming behind moving objects will be considered as objects. A better approach is to take the median of N first frames.
Simple subtraction may not be enough in cases of changing light condition etc. You may find a similarity criterion better for your application.
simple thresholding on the difference image may not be enough. A simple approach is to dilate the foreground for the sake of not updating the background on pixels that where accidentally identified as such.
Your step 4 is unclear, I assumed that you mean that you update the foreground only on those places that are identified as background on the last frame. Note that with such a simple approach, pixels that are actually background may be stuck forever with a "foreground" labeling, as you don't update the background under them. There are many possible solutions to this.
There are many ways to solve this problem, and it will really depend on the input images as to which method will be the most appropriate. It may be worth doing some reading on the topic
The method you are suggesting may work, but it's a slightly non-standard approach to this problem. My main concern would be that subtracting several images from the background could lead to saturation and then you may lose some detail of the motion. It may be better to take difference between consecutive images, and then apply the binarization / thresholding to these images.
Another (more complex) approach which has worked for me in the past is to take subregions of the image and then cross-correlate with the new image. The peak in this correlation can be used to identify the direction of movement - it's a useful approach if more then one thing is moving.
It may also be possible to use a combination of the two approaches above for example.
Subtract second image from the first background.
Threshold etc to find the ROI where movement is occurring
Use a pattern matching approach to track subsequent movement focussed on the ROI detected above.
The best approach will depend on you application but there are lots of papers on this topic
currently i am having much difficulty thinking of a good method of removing the gradient from a image i received.
The image is a picture taken by a microscope camera that has a light glare in the middle. The image has a pattern that goes throughout the image. However i am supposed to remove the light glare on the image created by the camera light.
Unfortunately due to the nature of the camera it is not possible to take a picture on black background with the light to find the gradient distribution. Nor do i have a comparison image that is without the gradient. (note- the location of the light glare will always be consistant when the picture is taken)
In easier terms its like having a photo with a flash in it but i want to get rid of the flash. The only problem is i have no way to obtaining the image without flash to compare to or even obtaining a black image with just the flash on it.
My current thought is conduct edge detection and obtain samples in specific locations away from the edges (due to color difference) and use that to gauge the distribution of gradient since those areas are supposed to have relatively identical colors. However i was wondering if there was a easier and better way to do this.
If needed i will post a example of the image later.
At the moment i have a preferrence of solving this in c++ using opencv if that makes it easier.
thanks in advance for any possible ideas for this problem. If there is another link, tutorial, or post that may solve my problem i would greatly appreciate the post.
as you can tell there is a light thats being shinned on the img as you can tell from the white spot. and the top is lighter than the bottome due to the light the color inside the oval is actually different when the picture is taken in color. However the color between the box and the oval should be consistant. My original idea was to perhaps sample only those areas some how and build a profile that i can utilize to remove the light but i am unsure how effective that would be or if there is a better way
EDIT :
Well i tried out Roger's suggestion and the results were suprisngly good. Using 110 kernel gaussian blurr to find illumination and conducting CLAHE on top of that. (both done in opencv)
However my colleage told me that the image doesn't look perfectly uniform and pointed out that around the area where the light used to be is slightly brighter. He suggested trying a selective gaussian blur where the areas above certain threshold pixel values are not blurred while the rest of the image is blurred.
Does anyone have opinions regarding this and perhaps a link, tutorial, or an example of something like this being done? Most of the things i find tend to be selective blur for programs like photoshop and gimp
EDIT2 :
it is difficult to tell with just eyes but i believe i have achieved relatively close uniformization by using a simple plane fitting algorithm.((-A * x - B * y) / C) (x,y,z) where z is the pixel value. I think that this can be improved by utilizing perhaps a sine fitting function? i am unsure. But I am relatively happy with the results. Many thanks to Roger for the great ideas.
I believe using a bunch of pictures and getting the avg would've been another good method (suggested by roger) but Unofruntely i was not able to implement this since i was not supplied with various pictures and the machine is under modification so i was unable to use it.
I have done some work in this area previously and found that a large Gaussian blur kernel can produce a reasonable approximation to the background illumination. I will try to get something working on your example image but, in the meantime, here is an example of your image after Gaussian blur with radius 50 pixels, which may help you decide if it's worth progressing.
UPDATE
Just playing with this image, you can actually get a reasonable improvement using adaptive histogram equalisation (I used CLAHE) - see comparison below - any use?
I will update this answer with more details as I progress.
I would like to point you to this paper: http://www.cs.berkeley.edu/~ravir/dirtylens.pdf, but, in my opinion, without any sort of calibration/comparison image taken apriori, it is difficult to mine out the ground truth from the flared image.
However, if you are trying to just present the image minus the lens flare, disregarding the actual scientific data behind the flared part, then you switch into the domain of image inpainting. Criminsi's algorithm, as described in this paper: http://research.microsoft.com/pubs/67276/criminisi_tip2004.pdf and explained/simplified in these two links: http://cs.brown.edu/courses/csci1950-g/results/final/eboswort/ http://www.cc.gatech.edu/~sooraj/inpainting/, will do a very good job in restoring texture information to the flared up regions. (If you'd really like to pursue this approach, do mention that. More comprehensive help can be provided for this).
However, given the fact that we're dealing with microscopic data, I doubt if you'd like to lose the scientific data contained in a particular region of an image. In that case, I really think you need to find a workaround to determine the flare model of the flash/light source w.r.t the lens you're using.
I hope someone else can shed more light on this.
Basicly i need to cut foreground object from green screen video. I need to make green transparent or directly cut the foreground object. I need to use OpenCv and C++. I find couple of methods but doesnt work. What i need to do it?
there isn't a magical way to do so. You need to programatically select the roi applying effects on each frame (i.e. on the Mat object). You may need to reduce noise, apply blur, extract each channels and do much more. So be patient and start experimenting.